{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys, os, _pickle as pickle\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import nltk\n",
    "from sklearn.metrics import f1_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data_dir = 'data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pos_tags_vocab = []\n",
    "for line in open('data/pos_tags.txt'):\n",
    "        pos_tags_vocab.append(line.strip())\n",
    "\n",
    "dep_vocab = []\n",
    "for line in open('data/dependency_types.txt'):\n",
    "    dep_vocab.append(line.strip())\n",
    "\n",
    "relation_vocab = []\n",
    "for line in open('data/relation_typesv3.txt'):\n",
    "    relation_vocab.append(line.strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "f = open('data/train_pathsv3', 'rb')\n",
    "words_seq, deps_seq, pos_tags_seq, word_path1, word_path2, dep_path1, dep_path2, pos_tags_path1, pos_tags_path2, pos_path1, pos_path2, childs_path1, childs_path2 = pickle.load(f)\n",
    "f.close()\n",
    "\n",
    "relations = []\n",
    "for line in open('data/train_relationsv3.txt'):\n",
    "    relations.append(line.strip().split()[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "most_common_rels = nltk.FreqDist(relations).most_common()\n",
    "\n",
    "for i, pos in enumerate(pos_tags_seq):\n",
    "    if i==0:\n",
    "        freq_pos = nltk.FreqDist(pos)\n",
    "    else:\n",
    "        freq_pos += nltk.FreqDist(pos)\n",
    "most_common_pos_tags = freq_pos.most_common()\n",
    "\n",
    "for i, dep in enumerate(deps_seq):\n",
    "    if i==0:\n",
    "        freq_dep = nltk.FreqDist(dep)\n",
    "    else:\n",
    "        freq_dep += nltk.FreqDist(dep)\n",
    "most_common_dep = freq_dep.most_common()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9C/tZkPPfJmnBaM/LzMx616+SwwsjYr+ImJMfnwqsjIhZwMr8GOBw0trRs4CFwNmQgglw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e507c5198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dependency Type :\n",
      "['root', 'nmod', 'nsubj', 'dobj', 'nsubjpass', 'compound', 'conj', 'acl', 'advcl', 'ccomp', 'amod', 'acl:relcl', 'xcomp', 'dep', 'appos', 'nmod:poss', 'advmod', 'parataxis', 'csubj', 'iobj']\n"
     ]
    }
   ],
   "source": [
    "deps = []\n",
    "freq_deps = []\n",
    "for f,v in most_common_dep[:25]:\n",
    "    deps.append(f)\n",
    "    freq_deps.append(v)\n",
    "\n",
    "indexes = np.arange(len(freq_deps))\n",
    "width = 5\n",
    "plt.bar(indexes, freq_deps, align='center', alpha=0.5,color='g')\n",
    "plt.xticks(indexes, deps, rotation='vertical')\n",
    "plt.show()\n",
    "print(\"Dependency Type :\")\n",
    "print(dep_vocab[:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ez/4vQ6ecHMzMkk5LeCX9Z3YdMbxCKmNNw/qo+f/lewInBzOzpNOqIUmXke5t\niIhtw9zN3cIXpM3Mdik/C2tn+dlhzUTEF4atRz3iIwczs6TTEt7XIycHMzPLvKHXHTAzs5HHycHM\nzDJODmZmlnFyMDOzzP8HWvsAkZzY878AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e508c0a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POS TAG vocab : ['CC', 'CD', 'DT', 'IN', 'JJ', 'NN', 'PRP', 'RB', 'VB']\n"
     ]
    }
   ],
   "source": [
    "pos_tags = []\n",
    "freq_pos_tags = []\n",
    "for f,v in most_common_pos_tags[:20]:\n",
    "    pos_tags.append(f)\n",
    "    freq_pos_tags.append(v)\n",
    "\n",
    "indexes = np.arange(len(freq_pos_tags))\n",
    "width = 5\n",
    "plt.bar(indexes, freq_pos_tags, align='center', alpha=0.5,color='r')\n",
    "plt.xticks(indexes, pos_tags, rotation='vertical')\n",
    "plt.show()\n",
    "print(\"POS TAG vocab :\", pos_tags_vocab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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569jf9gUdh30VcKKk0VU4lO7G/Sh787Zwg0rV0VEZ7LvTqATIhFa3PI+hVLfd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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2e507c27b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rel = []\n",
    "freq_rel = []\n",
    "for f,v in most_common_rels[:20]:\n",
    "    rel.append(f)\n",
    "    freq_rel.append(v)\n",
    "\n",
    "indexes = np.arange(len(freq_rel))\n",
    "width = 5\n",
    "plt.bar(indexes, freq_rel, align='center', alpha=0.5,color='b')\n",
    "plt.xticks(indexes, rel, rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
